Neuro-fuzzy ensemble approach for microarray cancer gene expression data analysis

Zhenyu Wang, Vasile Palade, Yong Xu

Research output: Chapter in Book/Report/Conference proceedingConference proceeding

52 Citations (Scopus)

Abstract

A Neuro-Fuzzy Ensemble model (NFE) is proposed in this paper for analysing the gene expression data from microarray experiments. The proposed approach was tested on three benchmark cancer gene expression data sets. Experimental results show that our NFE model can be used as an efficient computational tool for microarray data analysis. In addition, compared to some current most widely used approaches, Neuro-Fuzzy(NF)-based models not only supply good classification results, but their behavior can also be explained and interpreted in human understandable terms, which provides the researchers with a better understanding of the data.

Original languageEnglish
Title of host publicationProceedings of the 2006 International Symposium on Evolving Fuzzy Systems, EFS'06
PublisherIEEE
Pages241-246
Number of pages6
ISBN (Print)0780397193, 9780780397194
DOIs
Publication statusPublished - 30 Nov 2006
Externally publishedYes
Event2006 International Symposium on Evolving Fuzzy Systems, EFS'06 - Lake District, United Kingdom
Duration: 7 Sep 20069 Sep 2006

Conference

Conference2006 International Symposium on Evolving Fuzzy Systems, EFS'06
CountryUnited Kingdom
CityLake District
Period7/09/069/09/06

Fingerprint

Neuro-fuzzy
Microarrays
Gene Expression Data
Gene expression
Microarray
Data analysis
Cancer
Ensemble
Microarray Data Analysis
Model
Benchmark
Experimental Results
Term
Experiment
Experiments

ASJC Scopus subject areas

  • Artificial Intelligence
  • Software
  • Applied Mathematics
  • Theoretical Computer Science

Cite this

Wang, Z., Palade, V., & Xu, Y. (2006). Neuro-fuzzy ensemble approach for microarray cancer gene expression data analysis. In Proceedings of the 2006 International Symposium on Evolving Fuzzy Systems, EFS'06 (pp. 241-246). [4016708] IEEE. https://doi.org/10.1109/ISEFS.2006.251144

Neuro-fuzzy ensemble approach for microarray cancer gene expression data analysis. / Wang, Zhenyu; Palade, Vasile; Xu, Yong.

Proceedings of the 2006 International Symposium on Evolving Fuzzy Systems, EFS'06. IEEE, 2006. p. 241-246 4016708.

Research output: Chapter in Book/Report/Conference proceedingConference proceeding

Wang, Z, Palade, V & Xu, Y 2006, Neuro-fuzzy ensemble approach for microarray cancer gene expression data analysis. in Proceedings of the 2006 International Symposium on Evolving Fuzzy Systems, EFS'06., 4016708, IEEE, pp. 241-246, 2006 International Symposium on Evolving Fuzzy Systems, EFS'06, Lake District, United Kingdom, 7/09/06. https://doi.org/10.1109/ISEFS.2006.251144
Wang Z, Palade V, Xu Y. Neuro-fuzzy ensemble approach for microarray cancer gene expression data analysis. In Proceedings of the 2006 International Symposium on Evolving Fuzzy Systems, EFS'06. IEEE. 2006. p. 241-246. 4016708 https://doi.org/10.1109/ISEFS.2006.251144
Wang, Zhenyu ; Palade, Vasile ; Xu, Yong. / Neuro-fuzzy ensemble approach for microarray cancer gene expression data analysis. Proceedings of the 2006 International Symposium on Evolving Fuzzy Systems, EFS'06. IEEE, 2006. pp. 241-246
@inproceedings{d5b2adb65c204c23b832dbb639f104a0,
title = "Neuro-fuzzy ensemble approach for microarray cancer gene expression data analysis",
abstract = "A Neuro-Fuzzy Ensemble model (NFE) is proposed in this paper for analysing the gene expression data from microarray experiments. The proposed approach was tested on three benchmark cancer gene expression data sets. Experimental results show that our NFE model can be used as an efficient computational tool for microarray data analysis. In addition, compared to some current most widely used approaches, Neuro-Fuzzy(NF)-based models not only supply good classification results, but their behavior can also be explained and interpreted in human understandable terms, which provides the researchers with a better understanding of the data.",
author = "Zhenyu Wang and Vasile Palade and Yong Xu",
year = "2006",
month = "11",
day = "30",
doi = "10.1109/ISEFS.2006.251144",
language = "English",
isbn = "0780397193",
pages = "241--246",
booktitle = "Proceedings of the 2006 International Symposium on Evolving Fuzzy Systems, EFS'06",
publisher = "IEEE",
address = "United States",

}

TY - GEN

T1 - Neuro-fuzzy ensemble approach for microarray cancer gene expression data analysis

AU - Wang, Zhenyu

AU - Palade, Vasile

AU - Xu, Yong

PY - 2006/11/30

Y1 - 2006/11/30

N2 - A Neuro-Fuzzy Ensemble model (NFE) is proposed in this paper for analysing the gene expression data from microarray experiments. The proposed approach was tested on three benchmark cancer gene expression data sets. Experimental results show that our NFE model can be used as an efficient computational tool for microarray data analysis. In addition, compared to some current most widely used approaches, Neuro-Fuzzy(NF)-based models not only supply good classification results, but their behavior can also be explained and interpreted in human understandable terms, which provides the researchers with a better understanding of the data.

AB - A Neuro-Fuzzy Ensemble model (NFE) is proposed in this paper for analysing the gene expression data from microarray experiments. The proposed approach was tested on three benchmark cancer gene expression data sets. Experimental results show that our NFE model can be used as an efficient computational tool for microarray data analysis. In addition, compared to some current most widely used approaches, Neuro-Fuzzy(NF)-based models not only supply good classification results, but their behavior can also be explained and interpreted in human understandable terms, which provides the researchers with a better understanding of the data.

UR - http://www.scopus.com/inward/record.url?scp=34250773981&partnerID=8YFLogxK

U2 - 10.1109/ISEFS.2006.251144

DO - 10.1109/ISEFS.2006.251144

M3 - Conference proceeding

SN - 0780397193

SN - 9780780397194

SP - 241

EP - 246

BT - Proceedings of the 2006 International Symposium on Evolving Fuzzy Systems, EFS'06

PB - IEEE

ER -